\(\gamma \)  is here a generic kernel parameter. The weights \(w\)  are inferred by minimizing an \(L_2\)  regularized least squares problem which yields a closed form solution [1]}. We use the standard euclidean distance and the radial basis function kernel. The regularization parameter is varied between \(10^{-5}\)  and \(10^{5}\)  with one hundred logarithmically even spaced increments as well as the kernel parameter \(gamma\) . The combination with the best performance in the test set is chosen as final parameter set. Decision trees partition the feature space in greedy fashion and make predictions through the average value of training points within the partition to which a new data point \(x_{i}\)  is assigned. This partitioning is done in a sequential per-feature manner. The maximum tree depth, minimum number of samples per partition and minimum number of samples to induce a split are tested with values \(2^n, 1 \le n \le 5\)  by exhaustive combination. The final model is a multilayer perceptron. Here the model is a set of stacked layers consisting of individual units/neurons with the nonlinear activation function \(f(x)\) . Each neuron \((i,j)\)  is connected to all units \((i-1,k)\)  of the previous layer via a weighted connection of weight \(w\)  and is further modified with a bias \(b\) . The intermediate value in the i-th layer on the j-th neuron then is given by
\(z_{i,j}=f(\sum _{k}[b_{i,j} + w_{k,j}z_{i-1,k}])\) 
